Normalization and Outlier on Target variable which is continuous
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I have doubt that should I perform outlier analysis and normalization even on target variable which is continuous ?
data-cleaning
asked 13 hours ago
Navneeth Navneeth
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add a comment |
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I have doubt that should I perform outlier analysis and normalization even on target variable which is continuous ?
data-cleaning
asked 13 hours ago
Navneeth Navneeth
1
New contributor
Navneeth is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
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Kaggle competitions are won by doing precise outlier analysis of y compared to X. For example, we calculate y_hat based on LightGBM. Then we delete all y points outside of 2 standard dev. from y_hat and retrain. Repeat several times.
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– keiv.fly
10 hours ago
add a comment |
$begingroup$
I have doubt that should I perform outlier analysis and normalization even on target variable which is continuous ?
data-cleaning
asked 13 hours ago
Navneeth Navneeth
1
New contributor
Navneeth is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
I have doubt that should I perform outlier analysis and normalization even on target variable which is continuous ?
data-cleaning
data-cleaning
asked 13 hours ago
Navneeth Navneeth
1
New contributor
Navneeth is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 13 hours ago
Navneeth Navneeth
1
New contributor
Navneeth is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 13 hours ago
Navneeth Navneeth
1
New contributor
Navneeth is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 13 hours ago
Navneeth Navneeth
1
asked 13 hours ago
Navneeth Navneeth
1
1
New contributor
Navneeth is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
Navneeth is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
Navneeth is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$begingroup$
Kaggle competitions are won by doing precise outlier analysis of y compared to X. For example, we calculate y_hat based on LightGBM. Then we delete all y points outside of 2 standard dev. from y_hat and retrain. Repeat several times.
$endgroup$
– keiv.fly
10 hours ago
add a comment |
$begingroup$
Kaggle competitions are won by doing precise outlier analysis of y compared to X. For example, we calculate y_hat based on LightGBM. Then we delete all y points outside of 2 standard dev. from y_hat and retrain. Repeat several times.
$endgroup$
– keiv.fly
10 hours ago
$begingroup$
Kaggle competitions are won by doing precise outlier analysis of y compared to X. For example, we calculate y_hat based on LightGBM. Then we delete all y points outside of 2 standard dev. from y_hat and retrain. Repeat several times.
$endgroup$
– keiv.fly
10 hours ago
$begingroup$
Kaggle competitions are won by doing precise outlier analysis of y compared to X. For example, we calculate y_hat based on LightGBM. Then we delete all y points outside of 2 standard dev. from y_hat and retrain. Repeat several times.
$endgroup$
– keiv.fly
10 hours ago
add a comment |
1 Answer
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No, no need to perform outlier analysis and normalization on target variable for model performance or accuracy. (Though it might be useful to do some analysis on target variable to get some useful insights out of it)
Reasons behind performing normalization on input variables are as follows:
1)Feature scaling improves convergence of steepest descent algorithms
2)Helps to avoid a situation when several variables dominate other variables in magnitude
While if you normalize target variable, it, in turn, will normalize MSE and there will be no impact on results.
The only time when you might want to normalize target is the case of floating point overflow. Sometimes the number is too large or too small that CPU memory can't handle it and will turn into INF or wrap-around to the other extreme representation.
answered 13 hours ago
PreetPreet
2063
$endgroup$
$begingroup$
outlier analysis might help focusing the model on the 'more interesting' business cases, depends on the problem one is aiming to solve. It is not correct stating that outlier analysis is not needed.
$endgroup$
– yoav_aaa
12 hours ago
$begingroup$
Yes, correct. Updated the answer.
$endgroup$
– Preet
12 hours ago
$begingroup$
Preet, the first sentence in your answer is still very misleading. There may be different reasons to perform outlier analysis(decide on modeling strategy, pre processing, etc..). As a general practice it makes very much sense to preform such analysis.
$endgroup$
– yoav_aaa
12 hours ago
$begingroup$
Hi Preet, thanks for your inputs, but when I checked the link "stats.stackexchange.com/questions/111467/…" the last comment says that it's necessary to scale the target variable also, so I'm bit confused to come to a conclusion with the answers.
$endgroup$
– Navneeth
9 hours ago
add a comment |
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1 Answer
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$begingroup$
No, no need to perform outlier analysis and normalization on target variable for model performance or accuracy. (Though it might be useful to do some analysis on target variable to get some useful insights out of it)
Reasons behind performing normalization on input variables are as follows:
1)Feature scaling improves convergence of steepest descent algorithms
2)Helps to avoid a situation when several variables dominate other variables in magnitude
While if you normalize target variable, it, in turn, will normalize MSE and there will be no impact on results.
The only time when you might want to normalize target is the case of floating point overflow. Sometimes the number is too large or too small that CPU memory can't handle it and will turn into INF or wrap-around to the other extreme representation.
answered 13 hours ago
PreetPreet
2063
$endgroup$
$begingroup$
outlier analysis might help focusing the model on the 'more interesting' business cases, depends on the problem one is aiming to solve. It is not correct stating that outlier analysis is not needed.
$endgroup$
– yoav_aaa
12 hours ago
$begingroup$
Yes, correct. Updated the answer.
$endgroup$
– Preet
12 hours ago
$begingroup$
Preet, the first sentence in your answer is still very misleading. There may be different reasons to perform outlier analysis(decide on modeling strategy, pre processing, etc..). As a general practice it makes very much sense to preform such analysis.
$endgroup$
– yoav_aaa
12 hours ago
$begingroup$
Hi Preet, thanks for your inputs, but when I checked the link "stats.stackexchange.com/questions/111467/…" the last comment says that it's necessary to scale the target variable also, so I'm bit confused to come to a conclusion with the answers.
$endgroup$
– Navneeth
9 hours ago
add a comment |
$begingroup$
No, no need to perform outlier analysis and normalization on target variable for model performance or accuracy. (Though it might be useful to do some analysis on target variable to get some useful insights out of it)
Reasons behind performing normalization on input variables are as follows:
1)Feature scaling improves convergence of steepest descent algorithms
2)Helps to avoid a situation when several variables dominate other variables in magnitude
While if you normalize target variable, it, in turn, will normalize MSE and there will be no impact on results.
The only time when you might want to normalize target is the case of floating point overflow. Sometimes the number is too large or too small that CPU memory can't handle it and will turn into INF or wrap-around to the other extreme representation.
answered 13 hours ago
PreetPreet
2063
$endgroup$
$begingroup$
outlier analysis might help focusing the model on the 'more interesting' business cases, depends on the problem one is aiming to solve. It is not correct stating that outlier analysis is not needed.
$endgroup$
– yoav_aaa
12 hours ago
$begingroup$
Yes, correct. Updated the answer.
$endgroup$
– Preet
12 hours ago
$begingroup$
Preet, the first sentence in your answer is still very misleading. There may be different reasons to perform outlier analysis(decide on modeling strategy, pre processing, etc..). As a general practice it makes very much sense to preform such analysis.
$endgroup$
– yoav_aaa
12 hours ago
$begingroup$
Hi Preet, thanks for your inputs, but when I checked the link "stats.stackexchange.com/questions/111467/…" the last comment says that it's necessary to scale the target variable also, so I'm bit confused to come to a conclusion with the answers.
$endgroup$
– Navneeth
9 hours ago
add a comment |
$begingroup$
No, no need to perform outlier analysis and normalization on target variable for model performance or accuracy. (Though it might be useful to do some analysis on target variable to get some useful insights out of it)
Reasons behind performing normalization on input variables are as follows:
1)Feature scaling improves convergence of steepest descent algorithms
2)Helps to avoid a situation when several variables dominate other variables in magnitude
While if you normalize target variable, it, in turn, will normalize MSE and there will be no impact on results.
The only time when you might want to normalize target is the case of floating point overflow. Sometimes the number is too large or too small that CPU memory can't handle it and will turn into INF or wrap-around to the other extreme representation.
answered 13 hours ago
PreetPreet
2063
$endgroup$
No, no need to perform outlier analysis and normalization on target variable for model performance or accuracy. (Though it might be useful to do some analysis on target variable to get some useful insights out of it)
Reasons behind performing normalization on input variables are as follows:
1)Feature scaling improves convergence of steepest descent algorithms
2)Helps to avoid a situation when several variables dominate other variables in magnitude
While if you normalize target variable, it, in turn, will normalize MSE and there will be no impact on results.
The only time when you might want to normalize target is the case of floating point overflow. Sometimes the number is too large or too small that CPU memory can't handle it and will turn into INF or wrap-around to the other extreme representation.
answered 13 hours ago
PreetPreet
2063
edited 12 hours ago
answered 13 hours ago
PreetPreet
2063
answered 13 hours ago
PreetPreet
2063
answered 13 hours ago
PreetPreet
2063
2063
$begingroup$
outlier analysis might help focusing the model on the 'more interesting' business cases, depends on the problem one is aiming to solve. It is not correct stating that outlier analysis is not needed.
$endgroup$
– yoav_aaa
12 hours ago
$begingroup$
Yes, correct. Updated the answer.
$endgroup$
– Preet
12 hours ago
$begingroup$
Preet, the first sentence in your answer is still very misleading. There may be different reasons to perform outlier analysis(decide on modeling strategy, pre processing, etc..). As a general practice it makes very much sense to preform such analysis.
$endgroup$
– yoav_aaa
12 hours ago
$begingroup$
Hi Preet, thanks for your inputs, but when I checked the link "stats.stackexchange.com/questions/111467/…" the last comment says that it's necessary to scale the target variable also, so I'm bit confused to come to a conclusion with the answers.
$endgroup$
– Navneeth
9 hours ago
add a comment |
$begingroup$
outlier analysis might help focusing the model on the 'more interesting' business cases, depends on the problem one is aiming to solve. It is not correct stating that outlier analysis is not needed.
$endgroup$
– yoav_aaa
12 hours ago
$begingroup$
Yes, correct. Updated the answer.
$endgroup$
– Preet
12 hours ago
$begingroup$
Preet, the first sentence in your answer is still very misleading. There may be different reasons to perform outlier analysis(decide on modeling strategy, pre processing, etc..). As a general practice it makes very much sense to preform such analysis.
$endgroup$
– yoav_aaa
12 hours ago
$begingroup$
Hi Preet, thanks for your inputs, but when I checked the link "stats.stackexchange.com/questions/111467/…" the last comment says that it's necessary to scale the target variable also, so I'm bit confused to come to a conclusion with the answers.
$endgroup$
– Navneeth
9 hours ago
$begingroup$
outlier analysis might help focusing the model on the 'more interesting' business cases, depends on the problem one is aiming to solve. It is not correct stating that outlier analysis is not needed.
$endgroup$
– yoav_aaa
12 hours ago
$begingroup$
outlier analysis might help focusing the model on the 'more interesting' business cases, depends on the problem one is aiming to solve. It is not correct stating that outlier analysis is not needed.
$endgroup$
– yoav_aaa
12 hours ago
$begingroup$
Yes, correct. Updated the answer.
$endgroup$
– Preet
12 hours ago
$begingroup$
Yes, correct. Updated the answer.
$endgroup$
– Preet
12 hours ago
$begingroup$
Preet, the first sentence in your answer is still very misleading. There may be different reasons to perform outlier analysis(decide on modeling strategy, pre processing, etc..). As a general practice it makes very much sense to preform such analysis.
$endgroup$
– yoav_aaa
12 hours ago
$begingroup$
Preet, the first sentence in your answer is still very misleading. There may be different reasons to perform outlier analysis(decide on modeling strategy, pre processing, etc..). As a general practice it makes very much sense to preform such analysis.
$endgroup$
– yoav_aaa
12 hours ago
$begingroup$
Hi Preet, thanks for your inputs, but when I checked the link "stats.stackexchange.com/questions/111467/…" the last comment says that it's necessary to scale the target variable also, so I'm bit confused to come to a conclusion with the answers.
$endgroup$
– Navneeth
9 hours ago
$begingroup$
Hi Preet, thanks for your inputs, but when I checked the link "stats.stackexchange.com/questions/111467/…" the last comment says that it's necessary to scale the target variable also, so I'm bit confused to come to a conclusion with the answers.
$endgroup$
– Navneeth
9 hours ago
add a comment |
Navneeth is a new contributor. Be nice, and check out our Code of Conduct.
Navneeth is a new contributor. Be nice, and check out our Code of Conduct.
Navneeth is a new contributor. Be nice, and check out our Code of Conduct.
Navneeth is a new contributor. Be nice, and check out our Code of Conduct.
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$begingroup$
Kaggle competitions are won by doing precise outlier analysis of y compared to X. For example, we calculate y_hat based on LightGBM. Then we delete all y points outside of 2 standard dev. from y_hat and retrain. Repeat several times.
$endgroup$
– keiv.fly
10 hours ago